RRepoGEO

REPOGEO REPORT · LITE

alexeygrigorev/ai-engineering-field-guide

Default branch main · commit 9757d25b · scanned 6/19/2026, 1:42:39 AM

GitHub: 4,106 stars · 376 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface alexeygrigorev/ai-engineering-field-guide, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the MIT License text.
  • mediumhomepage#2
    Add a homepage URL to the repository About section

    Why:

    COPY-PASTE FIX
    https://alexeygrigorev.substack.com/

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface alexeygrigorev/ai-engineering-field-guide
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Python
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Python · recommended 1×
  2. Java · recommended 1×
  3. Scala · recommended 1×
  4. C++ · recommended 1×
  5. tensorflow/tensorflow · recommended 1×
  • CATEGORY QUERY
    What are the key responsibilities and required skills for an AI engineering role?
    you: not recommended
    AI recommended (in order):
    1. Python
    2. Java
    3. Scala
    4. C++
    5. TensorFlow (tensorflow/tensorflow)
    6. PyTorch (pytorch/pytorch)
    7. scikit-learn (scikit-learn/scikit-learn)
    8. Keras (keras-team/keras)
    9. Hugging Face Transformers (huggingface/transformers)
    10. AWS (Amazon Web Services)
    11. Amazon SageMaker
    12. AWS Lambda
    13. Amazon S3
    14. Amazon EC2
    15. Google Cloud Platform (GCP)
    16. Google Cloud AI Platform
    17. Google Kubernetes Engine (GKE)
    18. Google Cloud Storage
    19. Microsoft Azure
    20. Azure Machine Learning
    21. Azure Kubernetes Service (AKS)
    22. Azure Blob Storage
    23. Kubernetes (kubernetes/kubernetes)
    24. Docker (moby/moby)
    25. MLflow (mlflow/mlflow)
    26. DVC (Data Version Control) (iterative/dvc)
    27. SQL
    28. MongoDB (mongodb/mongo)
    29. Cassandra (apache/cassandra)
    30. Redis (redis/redis)
    31. Apache Spark (apache/spark)
    32. Kafka (apache/kafka)
    33. Git (git/git)

    AI recommended 33 alternatives but never named alexeygrigorev/ai-engineering-field-guide. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive guide for AI engineering interview preparation and learning paths?
    you: not recommended
    AI recommended (in order):
    1. Cracking the Coding Interview
    2. Deep Learning Interviews
    3. Towards Data Science
    4. LeetCode
    5. Deep Learning Specialization by Andrew Ng
    6. Machine Learning Engineering for Production (MLOps) Specialization
    7. Hugging Face

    AI recommended 7 alternatives but never named alexeygrigorev/ai-engineering-field-guide. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of alexeygrigorev/ai-engineering-field-guide?
    pass
    AI did not name alexeygrigorev/ai-engineering-field-guide — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts alexeygrigorev/ai-engineering-field-guide in production, what risks or prerequisites should they evaluate first?
    pass
    AI named alexeygrigorev/ai-engineering-field-guide explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo alexeygrigorev/ai-engineering-field-guide solve, and who is the primary audience?
    pass
    AI did not name alexeygrigorev/ai-engineering-field-guide — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of alexeygrigorev/ai-engineering-field-guide. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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alexeygrigorev/ai-engineering-field-guide — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite